Artificial intelligence system for estimating excess non-sapient payload capacity on mixed-payload aeronautic excursions

ABSTRACT

A system for selection of physical asset transfer paths using mixed-payload aeronautic excursions includes a client-interface module operating on at least a server, the client-interface module, configured to receive an initial location, a terminal location, and a description of at least an element of non-sapient payload, a path-selection module operating on the at least a server configured to identify at least an aeronautic path from the initial location to the terminal location and a plurality of aeronautic excursions traversing the at least an aeronautic path and select an aeronautic excursion of the plurality of aeronautic excursions based on a plurality of excess non-sapient payload storage estimations corresponding the plurality of aeronautic excursions, and a capacity estimation artificial intelligence module operating on the at least a server, the capacity estimation artificial intelligence module designed and configured to generate the plurality of excess non-sapient payload storage estimations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of Non-provisionalapplication Ser. No. 16/850,910 filed on Apr. 16, 2020, and entitled“ARTIFICIAL INTELLIGENCE SYSTEM FOR ESTIMATING EXCESS NON-SAPIENTPAYLOAD CAPACITY ON MIXED-PAYLOAD AERONAUTIC EXCURSIONS,” which is acontinuation-in-part of Non-provisional application Ser. No. 16/369,892filed on Mar. 29, 2019, and entitled “ARTIFICIAL INTELLIGENCE SYSTEM FORESTIMATING EXCESS NON-SAPIENT PAYLOAD CAPACITY ON MIXED-PAYLOADAERONAUTIC EXCURSIONS. Non-provisional application Ser. No. 16/850,910further claims the benefit of priority of U.S. Provisional PatentApplication Ser. No. 62/835,048, filed on Apr. 17, 2019, and titled“ARTIFICIAL INTELLIGENCE SYSTEM FOR ESTIMATING EXCESS NON-SAPIENTPAYLOAD CAPACITY ON MIXED-PAYLOAD AERONAUTIC EXCURSIONS,” each of whichare incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed to asystem for aeronautic path optimization and a method for its use.

BACKGROUND

Mixed-payload aeronautic excursions frequently carry different classesof payload in different sections or components of aircraft. Wheresapient payload is combined with associated non-sapient payload, asection for the latter may be left with excess capacity, which could beused to store additional non-sapient payload. However, this excesscapacity is difficult to predict and existing systems for suchestimation are inaccurate.

SUMMARY OF THE DISCLOSURE

In an aspect, A system for aeronautic path optimization is disclosed.The system comprises at least a server. At least a server is configuredto receive at least an order, wherein the order comprises data regardingthe physical transport of a non-sapient payload from an initial locationto a terminal location. The server may also be configured to identify anaeronautic path as a function of the at least an order using a pathselection module , wherein the aeronautic path comprises travel betweenthe initial location and the terminal location. The server may generatea plurality of optimal routes for the at least an order as a function ofthe aeronautic path. The server, produces an assigned route as afunction the plurality of optimal routes.

In another aspect, a method of use for aeronautic path optimization isdisclosed. The method comprises receiving, using at least a server, atleast an order, wherein the order comprises data regarding the physicaltransport of a non-sapient payload from an initial location to aterminal location. The method additionally comprises identifying, usingthe at least a server, an aeronautic path as a function of the at leastan order using a path selection module , wherein the aeronautic pathcomprises travel between the initial location and the terminal location.The method generates using the at least a server, a plurality of optimalroutes for the at least an order as a function of the produces, usingthe at least a server, an assigned route as a function of the pluralityof optimal routes.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for aeronautic path optimization;

FIG. 2 is a block diagram illustrating an exemplary embodiment of asystem for selection of physical asset transfer paths usingmixed-payload aeronautic excursions;

FIG. 3 is a block diagram illustrating an exemplary embodiment anartificial intelligence system for estimating excess non-sapient payloadcapacity on mixed-payload aeronautic excursions;

FIG. 4 is a block diagram illustrating exemplary embodiments ofdatabases that may be used to produce training data;

FIG. 5 is a block diagram of an exemplary embodiment of a pre-excursioninformation database;

FIG. 6 is a block diagram of an exemplary embodiment of an aircraftinformation database;

FIG. 7 is a block diagram of an exemplary embodiment of an aeronauticexcursion database;

FIG. 8 is a block diagram of an exemplary embodiment of an excursioncircumstance information database;

FIG. 9 is a block diagram of an exemplary embodiment of an excursioncommunication database;

FIG. 10 is a block diagram of an exemplary embodiment of a sapientpayload database;

FIG. 11 is a block diagram of an exemplary embodiment of a non-sapientpayload database;

FIG. 12 is a block diagram of an exemplary embodiment of a weightcapacity learner;

FIG. 13 is a block diagram of an exemplary embodiment of a volumecapacity learner;

FIG. 14 is a block diagram of an exemplary embodiment of a sapientpayload estimator;

FIG. 15 is a block diagram of an exemplary embodiment of a fuel uselearner;

FIG. 16 is a flow chart illustrating an exemplary embodiment of a methodof selection of physical asset transfer paths using mixed-payloadaeronautic excursions;

FIG. 17 is a block diagram of an exemplary machine-learning process;

FIG. 18 is a flow chart illustrating an exemplary embodiment of a methodof use for aeronautic path optimization; and

FIG. 19 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Embodiments of disclosed systems and methods use artificial intelligenceto generate modules that determine excess non-sapient payload capacityin mixed-payload aeronautic excursions. Resulting determinations may beused to estimate excess non-sapient payload capacity on futuremixed-payload aeronautic excursions. Data used to perform learningalgorithms may be provided in one or more databases, received via datafeeds from operators, airports, and/or weather services. Estimations maycontinually update in response to updated data.

Referring to FIG. 1 , an exemplary embodiment of a system 100 forselection of physical asset transfer paths using mixed-payloadaeronautic excursions. System 100 includes at least a server 104. Atleast a server 104 may include any computing device as described belowin reference to FIG. 19 , including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described below in reference to FIG. 19 . Atleast a server 104 may be housed with, may be incorporated in, or mayincorporate one or more sensors of at least a sensor. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. At least a server 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. At least a server 104 with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting a at least aserver 104 to one or more of a variety of networks, and one or moredevices. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Atleast a server 104 may include but is not limited to, for example, a atleast a server 104 or cluster of computing devices in a first locationand a second computing device or cluster of computing devices in asecond location. At least a server 104 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. At least a server 104 may distribute oneor more computing tasks as described below across a plurality ofcomputing devices of computing device, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. At least a server 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

At least a server 104 may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, at least a server 104 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. At least a server 104may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

With continued reference to FIG. 1 , a server 104 may be configured toreceive an order 108. As used in the current disclosure, an “order” iselement of data regarding the physical transfer of a non-sapient payloadfrom an initial location to a terminal location. As used in the currentdisclosure, a “non-sapient payload” is any non-human cargo to bedelivered. Examples of non-sapient payload may include goods, food,household items, clothing, footwear, jewelry, office supplies, cleaningsupplies, industrial supplies, groceries, and the like. Non-sapientpayload may be consistent with any package that may be delivered throughthe United States Postal Service. Non-sapient payload may also beconsistent with packages that may be delivered through services likeUPS, FedEx, DHL, Amazon, Door Dash, Postmates, Uber, Uber Eats, Lyft,and the like. An order may include both sapient payloads and non-sapientpayloads. Sapient payload may include a number of persons transported onthe aircraft besides flight crew such as pilots, co-pilots, flightattendants, security personnel, or the like; sapient payload mayinclude, for instance, patients being transported for medical purposes.Sapient payload may include prisoners being transported from onefacility to another. Sapient payload may include one or more personsthat have arranged to travel on an aircraft for an aeronautic excursion;one or more persons may be travelling for business, pleasure,governmental functions, or the like. One or more persons may include oneor more persons that have obtained a right of passage on the aircraftfor the aeronautic excursion by remunerative means, for instance byproviding currency, electronic payment, drafts, or the like to anoperator of the aircraft; one or more persons may be passengers.Non-sapient payload may include a quantity or number of elements ofpayload, as defined above, that are not sapient payload. For instance,and without limitation, non-sapient payload may include inanimateobjects, non-human living organisms such as animals, plants, or thelike, materials such as construction materials, or any other item oritems of payload that may be transported on an aircraft for anaeronautic excursion. Non-sapient payload may be expressed as a numberof items, a volume occupied by a single item or in the aggregate by aplurality of items including without limitation the set of all items tobe transported on the aircraft for at least a portion of the aeronauticexcursion, a weight of a single item, a weight of a plurality of itemsincluding without limitation the set of all items to be transported onthe aircraft for at least a portion of the aeronautic excursion, or anycombination thereof. Non-limiting examples of non-sapient payload mayinclude elements of non-sapient payload carried by one or more personsmaking up sapient payload, including without limitation suitcases,“carry-on” bags, backpacks, parcels, crates, chests, or the like and/orone or more elements of freight as described in further detail below.

With continued reference to FIG. 1 , an order 108 may comprise dataregarding an initial location 112 and a terminal location 116. As usedin the current disclosure, a “initial location” is the place ofdeparture for the non-sapient payload. As used in the current disclosurea “place of departure” is the original location of the non-sapientpayload within the system. As used in the current disclosure, a“terminal location” is the final destination for the non-sapientpayload. In a non-limiting example, an initial location and a terminallocation may comprise a business, residence, warehouse, factory, and thelike. In an embodiment, an order may be received from a user. Forinstance, and without limitation, a user may be a person who wishes tosend an item, which may include without limitation a parcel, shipment ofgoods, or shipment of products from a residence, business, factory,warehouse, or the like at initial location 112 to a residence, business,factory, warehouse, or the like at terminal location 116.

With continued reference to FIG. 1 , server 104 may classify the initiallocation 112 and/or terminal location 116 to a geographic centroid. Asused in the current disclosure, a “geographic centroid” is a centrallocation for the collection, distribution, and/or transportation oforders 108. A “central location,” as used in the current disclosure, isa location that is easily accessible for the collection, distribution,and transportation of orders 108. A central location may be determinedby the geographic location of the user, initial location, terminallocation, or infrastructure related to the collection distributionand/or transportation of orders 108. In some embodiments, geographiccentroid may comprise an order pick-up/drop off location such as a storefront. In other embodiments, a geographic centroid may comprise awarehouse, airport, factory, distribution center, and the like. Ageographic centroid may be configured to be with a pre-determinedgeographic radius of the initial location 112 or the terminal location116. Examples of a geographic radius may include a radius of 0.5 mile, 1mile, 2 miles, 5 miles, 10 miles, 15, miles, 30 miles, 50 miles, 100miles, and the like from either an initial location or a terminallocation. A non-sapient payload and/or order 108 may be configured to bedelivered to a geographic centroid prior to being delivered to aterminal location 116 or shortly after being received from an initiallocation 112.

With continued reference to FIG. 1 , server 104 may be configured toclassify geographic centroid to one or more the initial locations 112and/or terminal locations 116. As used in the current disclosure, a“centroid classifier” is a machine-learning model that sorts inputs intocategories or bins of data, outputting the categories or bins of dataand/or labels associated therewith. Centroid classifier may beconsistent with the classifier described below in FIG. 17 . Inputs tothe to the centroid classifier may include, as a non-limiting example,orders 108, initial location 112, terminal location 116, non-sapientpayload, examples of geographic centroids, any combination thereof, orthe like. The output to the centroid classifier may be a geographiccentroid that is specific to the given order 108. Centroid training datais a plurality of data entries containing a plurality of inputs that arecorrelated to a plurality of outputs for training a processor by amachine-learning process to align and classify one or more the initiallocations 112 and/or terminal locations 116 to a geographic centroid sp.Centroid training data may be received from a database. Centroidtraining data may contain information about , as a non-limiting example,orders 108, initial location 112, terminal location 116, non-sapientpayload, examples of geographic centroids, any combination thereof, orthe like. Centroid training data may be generated from any pastgeographic centroids. Centroid training data may correlate an example ofa geographic centroids to an example of an initial location 112 and/or aterminal location 116. The “example of a geographic centroids,” “exampleof an initial location,” and “example of an terminal location” may beprior a prior initial location 112, terminal location 116, and/orgeographic centroids, respectively. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naïve Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 1 , system 100 may include a path-selectionmodule 120 operating on at least a server 104. Path-selection module 120may include any suitable hardware or software module. In an embodiment,the path-selection module may be configured to identify at least anaeronautic path 124 from the initial location to the terminal location.As used herein, an “aeronautic path” is path of travel of orders betweenan initial location to a terminal location. This may also include thepath of travel of any goods, services, equipment, or personnel. In anembodiment, an aeronautic path 124 may include a chain of travel and/ortransport from a starting point, such as initial location, to adestination, such as terminal location, in which at least a stage of thetravel and/or transport is performed using an aeronautic excursion. Anaeronautic excursion, as used in this disclosure, may include any tripor voyage taken using an aircraft. Aeronautic excursion may be the sameor similar to a flight. Aircraft may include, without limitation, afixed-wing aircraft such as an airplane, a rotor-based aircraft such asa helicopter or autogiro, a lighter-than-air aircraft such as blimp ordirigible, or any other vehicle used to transport persons and/or goodthrough the air. Aeronautic excursion data may include data concerningmixed-payload aeronautic excursions. As used herein, a “mixed-payload”is a payload, defined as when anything carried on the aircraft besidesthe aircraft itself, materials used or usable for maintaining flight, orflight crew such as pilots, co-pilots, flight attendants, securitypersonnel, or the like, includes both sapient payload and non-sapientpayload. A “mixed-payload flight” is a flight or aeronautic excursionwherein the cargo is comprised of a mixed payload.

With continued reference to FIG. 1 , identification by path-selectionmodule 120 of at least an aeronautic path 124 may include identifying atleast an origin airport and at least a destination airport, defined,respectively as an airport at which an aeronautic excursion that is partof at least an aeronautic path 124 initially departs, and an airport atwhich an aeronautic excursion that is part of at least an aeronauticpath 124 arrives. At least an origin airport may include an airportrelatively close geographically or in terms of travel time to initiallocation; there may be a plurality of such origin airports. Forinstance, an order 108 may be located near a metropolitan area with twoairports, or near to a first airport in a first city and slightlyfarther from a second airport in a second city; path selection module120 may compare distances and/or times from initial location to airportsto thresholds, such as without limitation considering only airportswithin a one-hour drive of initial location. Threshold may alternativelyor additionally be a relative threshold; for instance, and withoutlimitation, path-selection module 120 may locate a nearest airport toinitial location and may consider as origin airports all airports lessthan a threshold amount more distant in terms of time and/or distancethan the nearest airport. Relative threshold may be used to select oneor more airports for which ground transport of items may be similar ifnot equivalent, presenting aeronautic paths 124 having roughlycomparable transit times. Path selection module 120 may select at leasta destination airport using any method or method steps suitable forselection of at least an origin airport, including comparison ofdistance and/or time in ground transport to absolute and/or relativethresholds as described above.

With continued reference to FIG. 1 , Path-selection module 120 may beconfigured to identify an aeronautic path 124 using a using a pathselection machine learning model. As used in the current disclosure, a“path selection machine learning model” is a mathematical and/oralgorithmic representation of a relationship between inputs and outputs.In some embodiments, a path selection machine learning model maycomprise a classifier. A path selection machine learning model may beconsistent with the machine learning model described herein below inFIG. 19 . Inputs to the machine learning model may include an order 108,an initial location 112, and a terminal location 116, examples ofaeronautic paths 124, and the like. This data may be received from adatabase, such as aeronautic excursion database 220, freight termsdatabase 232, ground transport database 236, and the like. Exampleaeronautic paths 124 may come from the previous selections of aeronauticpaths 124. Path selection learning model may by trained using pathselection training data. Path selection training data is a plurality ofdata entries containing a plurality of inputs that are correlated to aplurality of outputs for training a Path-selection module 120 by amachine-learning process. Path selection training data may correlate anexample of a aeronautic path 124 and an order to an aeronautic path 124.Path selection training data may include order 108, an initial location112, and a terminal location, examples of aeronautic paths 124, and thelike. Path selection training data may include past identifications ofaeronautic path 124. Path selection training data may be stored in adatabase, such as a training data database, or remote data storagedevice, or a user input or device.

Still referring to FIG. 1 , processor may be configured to generate amachine learning model, such as a path selection machine learning model,using a Naïve Bayes classification algorithm. Naïve Bayes classificationalgorithm generates classifiers by assigning class labels to probleminstances, represented as vectors of element values. Class labels aredrawn from a finite set. Naïve Bayes classification algorithm mayinclude generating a family of algorithms that assume that the value ofa particular element is independent of the value of any other element,given a class variable. Naïve Bayes classification algorithm may bebased on Bayes Theorem expressed as P(A/B)=P(B/A) P(A)÷P(B), where P(AB)is the probability of hypothesis A given data B also known as posteriorprobability; P(B/A) is the probability of data B given that thehypothesis A was true; P(A) is the probability of hypothesis A beingtrue regardless of data also known as prior probability of A; and P(B)is the probability of the data regardless of the hypothesis. A naïveBayes algorithm may be generated by first transforming training datainto a frequency table. Processor 104 may then calculate a likelihoodtable by calculating probabilities of different data entries andclassification labels. Processor 104 may utilize a naïve Bayes equationto calculate a posterior probability for each class. A class containingthe highest posterior probability is the outcome of prediction. NaïveBayes classification algorithm may include a gaussian model that followsa normal distribution. Naïve Bayes classification algorithm may includea multinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)α_(i) ²)}, whereα_(i) is attribute number experience of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance, be advantageous wherecases represented in training data are represented by differentquantities of samples, which may result in proportionally equivalentvectors with divergent values.

With continued reference to FIG. 1 , an aeronautic path 124 may comprisestops at one or more geographic centroids. An aeronautic path 124 mayinclude travel from an initial location 112 to one or more geographiccentroids. An aeronautic path 124 may also include travel from ageographic centroids to a terminal location 116. Aeronautic path 124 mayadditionally comprise travel from a first geographic centroid to asecond geographic centroid or any one of a plurality of geographiccentroids. A first geographic centroid may be located at a centrallocation near the initial location 112, while the second geographiccentroid may be located at a central location near the terminal location116. Travel between a geographic centroid, an initial location, and aterminal location may be done using ground transport, air transport, orthe use of one or more services such as the United States PostalService, FedEx, UPS, DHL, and the like. In an embodiment, stops at ageographic centroid may be used to increase the efficiency of theaeronautic path 124. In another embodiment, stops at a geographiccentroid may decrease the cost of transporting a non-sapient payloadfrom the initial location 112 to a terminal location 116.

With continued reference to FIG. 1 , server 104 may be configured togenerate an optimal route 128 for the at least an order 108 as afunction of the aeronautic path 120. As used in the current disclosure,an “optimal route” is the most efficient aeronautic path 120 to deliveran order from an initial location 112 to a terminal location 116.Efficiency may be measured with respect to arrival time, time intransit, time on shelf, cost, and the like. Arrival time may include thetime the order reaches the terminal destination. Time in transit mayinclude the total time between the order 108 leaving the initiallocation 112 until it reaches the terminal location 116. Total time intransit may include a consideration of weather conditions, traffic,arrival/departure of a flight, history of delays, and the like. Time onshelf may be a consideration of how long an order will be stored betweenthe time it leaves the initial location 112 until it reaches a consumer.In an embodiment there may be a plurality of optimal routes for somedegree of optimization and/or degree of similarity between optimizationlevels of a plurality. In an embodiment, a Path-selection module 120 mayidentify a plurality of aeronautic paths 124 to transport an order 108from an initial location 112 to a terminal location, Server 104 may beconfigured to identify one or more optimal routes 124 of the pluralityof aeronautic paths 124. Server 104 may identify the most optimal route128 as a function of the total time in transit of the order 108. In anon-limiting example, aeronautic paths 124 A, B, and C may be configuredto have a total transit time of 1:45 hours, 2:20 hours, and 3:00 hours,respectively. The optimal path 124 terms of transit time would be Abecause it has the shortest transit time in total. In furtherance of theabove non-limiting example, aeronautic paths 124 A, B, and C may arriveat a terminal location at 4:00 pm, 2:00 pm, and 10:00 am, respectively.optimal path 124 terms of arrival time would be aeronautic path 124 Cbecause it arrives earliest in the day.

With continued reference to FIG. 1 , server 104 may be configured togenerate an optimized route 128 from a plurality of aeronautic paths 124using a using an optimization machine learning model. As used in thecurrent disclosure, a “optimization machine learning model” is amathematical and/or algorithmic representation of a relationship betweeninputs and outputs. In some embodiments, a optimization machine learningmodel may comprise a classifier, linear optimization, mixed-integeroptimization, and/or greedy algorithm. An optimization machine learningmodel may be consistent with the machine learning model described hereinbelow in FIG. 19 . Inputs to the machine learning model may include anorder 108, an initial location 112, and a terminal location 116, aplurality of optimized route 128 examples of an optimized route 128, andthe like. Outputs to the optimization machine learning model may includean optimal route 128 specific to the current order. This data may bereceived from a database, such as aeronautic excursion database 220,freight terms database 232, ground transport database 236, and the like.Examples of optimized routes 128 may come from the previous selectionsof optimized routes 128. Optimization machine learning model may bytrained using optimization training data. Optimization training data isa plurality of data entries containing a plurality of inputs that arecorrelated to a plurality of outputs for training an optimizationmachine-learning model by a machine-learning process. Optimizationtraining data may correlate an example of an optimized route 128 and aplurality of aeronautic paths 124 to an optimized route 128.Optimization training data may also correlate an initial location112,terminal location 116, and aeronautic path 124 to an optimized route128. Optimization training data may include order 108, an initiallocation 112, and a terminal location 116, a plurality of optimizedroutes 128, examples of an optimized route 128, and the like.Optimization training data may include past identifications ofaeronautic path 124. Optimization training data may be stored in adatabase, such as a training data database, remote data storage device,or a user device.

Still referring to FIG. 1 , an optimal route ranking may be generated asa function of the optimized route 128 and the plurality aeronautic paths124. As used in the current disclosure, a “Optimal route ranking ” is aranking of how efficiency of a given aeronautic path 124. A optimalroute ranking may be generated with respect to arrival time, time intransit, time on shelf, cost, and the like. A optimal route ranking maybe calculated on a scale from 1-10, wherein a rating of 1 may be a aninefficient aeronautic paths 124 whereas a rating of 10 may be anextremely efficient aeronautic paths 124. A optimal route ranking may begenerated from optimized route 128 and the plurality aeronautic paths124. In some embodiments, generating the optimal route ranking mayinclude linear regression techniques. Server 104 may be designed andconfigured to create a machine-learning model using techniques fordevelopment of linear regression models. Linear regression models mayinclude ordinary least squares regression, which aims to minimize thesquare of the difference between predicted outcomes and actual outcomesaccording to an appropriate norm for measuring such a difference (e.g.,a vector-space distance norm); coefficients of the resulting linearequation may be modified to improve minimization. Linear regressionmodels may include ridge regression methods, where the function to beminimized includes the least-squares function plus term multiplying thesquare of each coefficient by a scalar amount to penalize largecoefficients. Linear regression models may include least absoluteshrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius norm mountingto the square root of the sum of squares of all terms. Linear regressionmodels may include the elastic net model, a multi-task elastic netmodel, a least angle regression model, a LARS lasso model, an orthogonalmatching pursuit model, a Bayesian regression model, a logisticregression model, a stochastic gradient descent model, a perceptronmodel, a passive aggressive algorithm, a robustness regression model, aHuber regression model, or any other suitable model that may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure. Linear regression models may be generalized in an embodimentto polynomial regression models, whereby a polynomial equation (e.g., aquadratic, cubic or higher-order equation) providing a best predictedoutput/actual output fit is sought; similar methods to those describedabove may be applied to minimize error functions, as will be apparent topersons skilled in the art upon reviewing the entirety of thisdisclosure.

With continued reference to FIG. 1 , a server 104 may be configured toproduce an assigned route 132. As used in the current disclosure, an“assigned route” is the optimized route 128 that an order 108 will takefrom an initial location to a terminal location. An assigned route 132may be selected based on timing. An assigned route 132 may also beselected as a function of the assigned route 132 of other orders 108that will travel the same aeronautic path 124 as the current order.aeronautic paths 124 In a non-limiting example, if an optimized route128 of an order 108 would cause the other packages to be delayed, themost optimal route 128 may not be selected as the assigned route 128because of the disturbance to the other packages. In this scenario,server 104 may select the second most optimal route 128, because thisroute is less invasive to other orders 108 that the most optimal routes128. In some embodiments, server 104 may not select an assigned route132 of the plurality of optimal routes 128. In this non-limitingscenario, server 104 may place the rejection to select an assigned route136 of the through the optimization machine learning model in a feedbackloop. As used in the current disclosure, a “feedback loop” is a processin which the outputs of a system are circled back and used as inputs.This may result in the identification of a second optimal route 128. Thesecond optimal route 128 might be based on an objective function oftotal delays in a cluster or region and optimization of each individualroute.

With continued reference to FIG. 1 , a server 104 may be configured toproduce an assigned route 132 as a function of an optimal route ranking.In an non-limiting example, a plurality of optimized routes 124 A, B,and C may have an optimized route ranking of 2, 6, 8, respectively. Aserver 104 may be configured to select optimized route 124 C as theassigned route 132 because it has the highest optimized route ranking ofthe plurality of optimized routes 124. In some embodiments, if there isa timing conflict with the optimal route 124 with the highest optimizedroute ranking the optimal route 124 with the second highest ranking maybe selected. In furtherance of the above non-limiting example, ifoptimized route 124 C conflicts with the assigned route 132 of otherorders 108 delaying them or creating other inefficiencies optimizedroute 124 B may be selected as the assigned route 136 of the order 108.

Referring now to FIG. 2 , system 200 may include a client-interfacemodule 204 operating on the at least a server. Client-interface module204 may include any suitable hardware or software module. In anembodiment, client-interface module 204 is configured to receive aninitial location, a terminal location, and a description of at least anelement of non-sapient payload from a client device 208. Client device208 may include any computing device as described below in reference toFIG. 19 . In an embodiment, client device 208 may be a device operatedby a user interacting with system 200 to perform a physical transfer ofitems from initial location to terminal location. For instance, andwithout limitation, user may be a person who wishes to send an item,which may include without limitation a parcel, shipment of goods, orshipment of products from a residence, business, factory, warehouse, orthe like at initial location to a residence, business, factory,warehouse, or the like at terminal location. Client-interface module 204may provide any suitable user interface via client device 208 for entryof information, including a graphical user interface (GUI) such as aweb-page GUI, a GUI provided via a native application running on clientdevice 208, or the like. GUI may include one or more fields for dataentry, including textual entry fields in which text may be typed,entered by voice-to-text data entry, or the like, drop-down menuslisting options, calendar entry fields for entry of dates and/or times,radio buttons, checkboxes, links, or any other event handlers and/or GUIfields or elements for entry of data and/or user commands that may occurto a person skilled in the art, upon reviewing the entirety of thisdisclosure.

In an embodiment, and continuing to refer to FIG. 2 , client-interfacemodule 204 and/or GUI may provide fields for user entry of and/orreceive one or more additional elements of information. For example, andwithout limitation, a user may enter, via client device 208 and/orclient-interface module 204, one or more temporal attributes of aphysical transfer of one or more items, including a date and/or time forarrival of the one or more items at terminal location, a date and/ortime for arrival of the one or more items at initial location, and/or ordates or times during which one or more stages of physical transfer areto be completed as described in further detail below. User may enter,via client device 208 and/or client-interface module 204, one or moreitem handling conditions, such as a degree of care to which one or moreitems should be subjected during transfer, a degree of fragility of oneor more items, an exceptional parameter such as excessive weight or anunwieldy or unusual shape or one or more items, special conditions fortransfer such as refrigeration, heating, insulation, or the like duringtransfer, care requirements for living organisms to be transferred,and/or any other conditions for handling attendant to a physicaltransfer of items as may occur to persons skilled in the art uponreviewing the entirety of this disclosure. Additional informationentered via client device 208 and/or client-interface module 204 mayinclude weights of one or more items, with or without packaging.Additional information entered via client device 208 and/orclient-interface module 204 may include volumes of one or more items,with or without packaging. Additional information entered via clientdevice 208 and/or client-interface module 204 may include one or morecost parameters, including a maximum desired price or the like.Information entered via client device 208 and/or client-interface module204 may include any details concerning physical transfers of items thatmay occur to persons skilled in the art upon reviewing the entirety ofthis disclosure.

Still referring to FIG. 2 , path selection module 120 may be configuredto identify a plurality of aeronautic excursions traversing the at leastan aeronautic path 124. In an embodiment, path selection module 120 mayretrieve data describing one or more aeronautic excursions from anaeronautic excursion database 212, which may contain any tables and/orinformation as described in further detail below, including withoutlimitation aeronautic excursion times, origin airports, destinationairports, stopovers, or the like. Aeronautic excursion data mayalternatively or additionally be received from an airline device 216operated by an airline or one or more persons working for or with anairline, for instance via client-interface module 204, or from anoperator data feed and/or airport data feed as described in furtherdetail below. Identifying the one or more aeronautic excursions mayinclude identifying any feature of any aeronautic excursions, includingone or more departure times, arrival times, quantities of flight time,layover times, one or more aircraft engaging in the at least anaeronautic excursion, or the like. Identifying at least an aeronauticexcursion may include identifying one or more interim airports, whichmay include any airports at which an aircraft engaging in at least anaeronautic excursion may land during the course of the aeronauticexcursion, such as “stopover” points. At least an aeronautic excursionmay include more than one plane and/or airline.

In an embodiment, and still referring to FIG. 2 , path-selection module120 may be configured to select an aeronautic excursion of the pluralityof aeronautic excursions based on a plurality of excess non-sapientpayload storage estimations corresponding the plurality of aeronauticexcursions. Each non-sapient payload storage estimation may bedetermined for one aeronautic excursion of plurality of aeronauticexcursions. A capacity estimation AI module 220 may determine and/orestimate non-sapient payload storage capacity as described in furtherdetail below in reference to FIG. 3 . Selection may include selection ofa subset of plurality of aeronautic excursions having sufficientcapacity to perform the physical transfer of items as required byinformation received via client-interface module 204.

Continuing to refer to FIG. 2 , path-selection module 120 mayalternatively or additionally select an aeronautic excursion fromplurality of aeronautic excursions using one or more additionalcriteria. One or more additional criteria may include any additionaldata concerning aeronautic excursion of at least an aeronauticexcursion. Selection based on additional data may include withoutlimitation selection based on one or more times or distances. As anon-limiting example, selection may include selection based on lengthand/or duration of at least an aeronautic excursion, such as totalflight time, total time from origin airport to destination airport,total flight distance, and/or total distance from origin airport todestination airport; any such criteria may be retrieved from aeronauticexcursion database 212, received from airline device 216, and/or via anoperator data feed and/or airport data feed as described in furtherdetail below. One or more additional criteria may include one or morefreight terms, defined for the purposes of this disclosure as datadescribing parameters according to which one or more airlines are ableand/or willing to engage in freight transport. One or more freight termsmay include, without limitation, costs charged by airlines, such ascosts per unit weight, costs per unit volume, costs per category ofitem, costs for handling options such as handling for fragile goods,perishable items, refrigerated items, heated items, living creaturessuch as plants and/or animals, human remains, human organs,pharmaceuticals, hazardous materials, or the like. One or more freightterms may include, without limitation, capabilities to transport one ormore categories of goods, such as fragile goods, perishable items,refrigerated items, heated items, living creatures such as plants and/oranimals, human remains, human organs, pharmaceuticals, hazardousmaterials, or the like. One or more freight terms may be received froman airline device 216 and/or one or more airport and/or operator feeds.Alternatively or additionally, one or more freight terms may beretrieved from a freight terms database 224; freight terms database 224may include any database and/or datastore suitable for use as any otherdatabase as described in this disclosure. Freight terms database 224 maybe populated using any suitable methods and/or sources of data,including without limitation airline device 216 and/or one or moreairport and/or operator feeds.

As a further non-limiting example, and still referring to FIG. 2 ,selection may include selection based on one or more parameters ofground transport. One or more parameters of ground transport may includedistances and/or times to be covered by ground transport from initiallocation to origin airport and/or to an accepting warehouse, distancesor times from an accepting warehouse to an aeronautic excursion,distances and/or times to be covered from a destination airport to areceiving warehouse and/or terminal location, and/or distances and/ortimes to be covered from a receiving warehouse to terminal location;such distances and/or times may be retrieved from a ground transportdatabase 228 and/or calculated using data from ground transport database228. For instance, timetable information, locations of ground transportheadquarter locations, pickup times and/or frequencies may be retrievedfrom ground transport database 228 and combined with route calculationand/or map software to determine a likely distance and/or time asdescribed above. Information may alternatively or additionally bereceived from a ground transport device 232, which may, for instance,provide data to populate ground transport database 228 and/or real-timedata such as current locations of ground transport vehicles asdetermined using global positioning system (GPS) calculations,communications with vehicles' mobile computing devices, or the like.

Continuing to refer to FIG. 2 , selection may include selection based onone or more cost of ground transport, such as costs per unit weight,costs per unit volume, costs per category of item, costs for handlingoptions such as handling for fragile goods, perishable items,refrigerated items, heated items, living creatures such as plants and/oranimals, human remains, human organs, pharmaceuticals, hazardousmaterials, or the like. One or more costs may be retrieved from groundtransport database 228 and/or received from a ground transport device232. Selection may include selection based on one or more groundtransport parameters, including without limitation capabilities totransport one or more categories of goods, such as fragile goods,perishable items, refrigerated items, heated items, living creaturessuch as plants and/or animals, human remains, human organs,pharmaceuticals, hazardous materials, or the like. One or more groundtransport parameters may be retrieved from ground transport database 228and/or received from a ground transport device 232.

Still referring to FIG. 2 , selection may include selection based on oneor more overall values, such as without limitation door to door durationof an aeronautic excursion, defined as the total time from pickup of oneor more items as described via client-interface module at initiallocation to drop-off at terminal location; door to door duration mayinclude any time spent in ground transit, in an accepting warehouse,during security inspection such as without limitation manual, chemical,and/or x-ray inspection, on an aircraft, at an airport, at a receivingwarehouse, or the like. One or more overall values may includedoor-to-door distance, including distance traversed during groundtransit, on an aircraft, or the like. One or more overall values mayinclude total cost, including fees and/or costs for ground transport,fees and/or costs for use of an accepting warehouse, fees and/or costsfor security inspection such as without limitation manual, chemical,and/or x-ray inspection, fees and/or costs charged for use of cargospace on an aircraft, fees and/or costs for an airport, fees and/orcosts for use of a receiving warehouse, or the like. Any aggregatevalues that are part of any overall costs may alternatively oradditionally be used in selection of an aeronautic excursion. Overallvalues or aggregate values may be compared to one or more thresholds asdescribed above, such as maximum door-to-door transit time, minimumcost, or the like. Each value used in selection, including overalland/or aggregate values, as well as any other values as described above,may include a fixed value, an estimated value, and/or a forecastedvalue, where forecasting and/or estimation may be performed, withoutlimitation, using machine-learning processes and/or artificialintelligence as described in further detail below. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various alternative or additional ways that overall and/or aggregatevalues may be computed and/or used in selection, each of which isconsidered within the scope of this disclosure.

In an embodiment, and still referring to FIG. 2 , selection of anaeronautic excursion from the plurality of aeronautic excursions mayinclude display and/or providing information describing at least anaeronautic excursion to a user; this may be performed byclient-interface module 204 via a client device 208. In an embodiment, auser may be provided with a plurality of aeronautic excursions selectedusing one or more processes and/or criteria as described above.Plurality may be presented to user in the form of options to select foreach stage of an aeronautic path 124 to be traversed; for instance, usermay be provided with one or more options for ground transport, forreceiving warehouse, for origin airport, for destination airport, forinterim airports, for flights from or to any airport, and/or foraccepting warehouse, from which user may select a preferred option ateach stage. Options and/or aeronautic excursions may be provided to theuser with any data concerning such options and/or aeronautic excursions,including data useable to make selections as described above, entitiesperforming any step or stage to which an option is applicable, or thelike; for instance, a user may select ground transport and/or anaircraft or flight based on cost, an entity performing the step orstage, an overall duration or distance to be covered, ability to performspecific handling needs, or the like. A user may select one aeronauticexcursion from the plurality of aeronautic excursions using a link,button, or other event handler that may occur to persons skilled in theart upon reviewing the entirety of this disclosure.

Still referring to FIG. 2 , system may be configured to contact one ormore entities that perform one or more selected steps and/or stages of aselected aeronautic excursion. For instance, at least a server 104,potentially via client-interface module, may convey data to an airlinedevice 216, ground transport device 232, and/or other computing deviceoperated by a person or entity selected to perform a given step or stageof a selected aeronautic excursion; data may include a request toperform the given step or stage according to parameters provided by theperson or entity, which may be any parameters as described above,including estimated duration, distance, cost, handling instructions, orthe like. System 200 may receive confirmation from user or entityindicating that terms are accepted. System 200 may receive one or moreindications that aeronautic excursion, and/or a step or stage inselected aeronautic excursion has been undertaken; such indications maybe provided to client device 208 and/or stored in one or more databasesand/or training sets as described below. Indications that aeronauticexcursion, and/or a step or stage in selected aeronautic excursion hasbeen undertaken may include, without limitation, actual durations,distances, conditions and/or circumstances of aeronautic excursionand/or step or stage thereof, each of which may be used to update anyapplicable database as described in this disclosure, to update trainingdata as described in further detail below, and/or to perform new orupdated machine-learning tasks as described in further detail below.

Referring now to FIG. 3 , an artificial intelligence system 300 forpredicting excess cargo capacity on passenger flights is illustratedestimating excess non-sapient payload capacity on mixed-payloadaeronautic excursions is illustrated. System 300 may be incorporated insystem 200, and/or may be in communication with system 200; one or morecomponents of system 300 may be incorporated in part or wholly in system200, and/or may be in communication with one or more components ofsystem 200. As a non-limiting example, system 300 may include at least aserver 104 and/or components of system 300 may operate on at least aserver 104; alternatively or additionally, part or all of system 300and/or any component thereof may operate on a different server and/orone or more additional computing devices (not shown). For the sake ofclarity, description of system 300 will be made with reference to atleast a server 104 and to capacity estimation artificial intelligencemodule as described above. In an embodiment, at least a server 104 maybe designed and configured to produce a corpus of aeronautic excursiondata 300. At least a corpus of aeronautic excursion data 300 may includeat least a corpus of training data. Training data, as used herein, isdata containing correlation that a machine-learning process may use tomodel relationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 3 , trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

With continued reference to FIG. 3 , at least a corpus of aeronauticexcursion data 300 includes a plurality of aeronautic excursion dataentries. Each data entry of plurality of aeronautic excursion dataentries may include data pertaining to an aeronautic excursionAeronautic excursion data may include data concerning mixed-payloadaeronautic excursions as described above.

With continued reference to FIG. 3 , each aeronautic excursion dataentry 304 correlates at least a first aeronautic excursion parameterdatum 308 and at least a second aeronautic excursion parameter datum312. As used in this disclosure, an aeronautic excursion parameter datumis any element of data including any facet of an aeronautic excursion.An aeronautic excursion parameter datum may include any data that wasprovided prior to an aeronautic excursion; for instance, and withoutlimitation, aeronautic excursion parameter datum may include withoutlimitation data included in a load distribution message (LDM), datacontained in a container pallet message (CPM), fuel message, flight planinformation including distance to be traveled, number of stops and/orlayovers, an origin airport, a destination airport, a flight path to befollowed, one or more dates and/or times including times of departure,arrival, layovers, or the like, a time of day that an aeronauticexcursion was intended to occupy, and/or a planned duration of flight.

Continuing to refer to FIG. 3 , an aeronautic excursion parameter datummay include any data concerning and/or describing an aircraft used in anaeronautic excursion, including without limitation data identifying anentity and/or person operating the aeronautic excursion, such as anairline company and/or owner, pilots crew, maintenance, cleaning,luggage handling, and/or repair personnel at any airport, securitypersonnel on the aircraft of at any airport, an identifier identifyingthe aeronautic excursion such as a “flight ID,” a variety of aircraftundertaking the aeronautic excursion, including without limitation amake, model, manufacturer, or the like, a date of manufacture, a lot ofmanufacture, a number of years of use and/or operation of the aircraft,a number representing total flying time the aircraft had undergone as ofthe beginning of the aeronautic excursion, one or more numbersrepresenting an amount of flying time undergone by an aircraft per unitof time such as a decade, year, month, week, or day, and/or maintenancehistory, including without limitation history of repairs, replacement ofparts or components, incidents requiring grounding, emergency landing,diversion, or repairs, or the like. Aircraft data may also includecompartment data; for instance, an aircraft used in mixed-payloadaeronautic excursions may have a fuselage divided into, for instance, acompartment with seating for persons, a compartment for transport ofnon-sapient payload items, including without limitation a “luggagecompartment,” and/or one or more additional compartments used fornon-sapient payload items, such as “carry-on” stowage locations.

Still referring to FIG. 3 , an aeronautic excursion parameter datum mayinclude, without limitation, aeronautic excursion circumstances data,which may include any information concerning circumstances of aeronauticexcursion external to an aircraft; such data may include withoutlimitation information describing weather reports in areas traversedduring the aeronautic excursion, weather forecasts in areas traversedduring the aeronautic excursion, geographical data concerning areastraversed during the aeronautic excursion, a season during which theaeronautic excursion took place, a calendar date on which the aeronauticexcursion took place, a time of day during which the aeronauticexcursion took place, a degree of congestion at one or more airportsfrom which the aircraft departed and/or at which the aircraft arrivedduring the aeronautic excursion, or the like.

Continuing to refer to FIG. 3 , an aeronautic excursion parameter datummay include, without limitation, aeronautic excursion data, which may beany data describing how an aeronautic excursion took place. Aeronauticexcursion data may include, without limitation, information describingan actual flight plan followed, including distance that was traveled,number of stops and/or layovers that took place, actual origin airport,actual destination airport, an actual flight path that was traversed,one or more actual dates and/or times including times of departure,arrival, actual layovers, a time of day that an aeronautic excursionoccupied, actual duration of the aeronautic excursion, flight delayinformation, landing delay information, passenger feedback, and/or datadescribing state of non-sapient payload items upon arrival.

Still referring to FIG. 3 , an aeronautic excursion parameter datum mayinclude aeronautic excursion communication data, which is defined asdata describing electronic communication performed during an aeronauticexcursion. Aeronautic excursion communication data may include, withoutlimitation, communication logs describing communications between pilotsand other entities and/or persons such as without limitation air trafficcontrollers. Aeronautic excursion communication data may include withoutlimitation transponder-based communication. Aeronautic excursioncommunication data may include automated dependentsurveillance-broadcast (ADS-B) data including ADS-B-In or ADS-B-Outdata. Aeronautic excursion communication data may include flightrecorder data. Generally, aeronautic excursion parameter datum mayinclude any other example of aeronautic excursion parameter datadescribed or alluded to in this disclosure, as well as any other exampleor examples that may occur to a person of ordinary skill in the art uponreviewing the entirety of this disclosure.

Continuing to refer to FIG. 3 , an aeronautic excursion parameter datummay include, without limitation, sapient payload data. Sapient payloaddata may include, without limitation, any information concerning personsmaking up sapient payload, including individual body weights, averagebody weights, and/or total body weight, demographic information such asindividual ages, average ages, countries, provinces, territories,departments, and/or municipalities of origin, frequency of travel,purpose of travel, or the like. Sapient payload data may be linked tonon-sapient payload data as described in further detail below; forinstance, sapient payload data may include data describing amounts ofnon-sapient payload brought onto an aircraft by one or more persons,and/or an aggregated total of non-sapient data brought onto the aircraftby all persons represented by sapient payload data.

Still referring to FIG. 3 , an aeronautic excursion parameter datum mayinclude, without limitation, non-sapient payload data. Non-sapientpayload data may include, without limitation, any information concerningitems making up non-sapient payload, individual weights of items,average weights of items, and/or total weight of all non-sapientpayload. Non-sapient payload data may include, without limitation,individual volumes of items, volumes of storage taken up by individualitems, average volumes of items, average volumes of storage space takenup by items, total volume of all non-sapient payload, and/or totalvolume of storage space taken up by non-sapient payload. Non-sapientpayload data may be linked to sapient payload data as described infurther detail below; for instance, non-sapient payload data may includedata describing amounts of non-sapient payload brought onto an aircraftby one or more persons, and/or an aggregated total of non-sapient databrought onto the aircraft by all persons represented by sapient payloaddata.

Referring now to FIG. 4 , data incorporated in corpus of aeronauticexcursion data 300 may be incorporated in one or more databases. As anon-limiting example, one or more elements of pre-excursion informationmay be stored in and/or retrieved from a pre-excursion informationdatabase 400. A pre-excursion information database 400 may include anydata structure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. A pre-excursioninformation database 400 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. Pre-excursion information database 400 mayinclude, without limitation, any data that was provided prior to anaeronautic excursion as described above.

Referring now to FIG. 5 , one or more database tables in pre-excursioninformation database 400 may include, as a non-limiting example an LDMtable 500 listing LDM information as described above. One or more tablesmay include a CPM table 504 listing CPM data as described above. One ormore tables may include a fuel message table 508, which may list fuelmessage data as described above. One or more tables may include flightplan table 512 listing flight plan information as described above.

Referring again to FIG. 4 , one or more elements of aircraft data may bestored in and/or retrieved from an aircraft information database 404.

Referring now to FIG. 6 , one or more database tables in aircraftinformation database 404 may include, as a non-limiting example anaircraft model table 600, which may describe a make and model of anaircraft. One or more tables may include an operator table 604, whichmay describe, without limitation, entities, pilots, and crew operatingthe aircraft, as described above. One or more tables may include aflying history table 608, which may describe flying time, number ofaeronautic excursions, and/or other data concerning flight history of anaircraft. One or more tables may include a maintenance history table612, which may describe a history of maintenance as described above.

Referring again to FIG. 4 , one or more elements of aeronautic excursiondata as described above may be stored in and/or retrieved fromaeronautic excursion information database 212. Referring now to FIG. 7 ,one or more database tables in aeronautic excursion information database212 may include, as a non-limiting example, a flight plan table 700,which may list flight plan information actually followed during anaeronautic excursion, a temporal information table 704 listing dates andtimes an aeronautic excursion took place, an airport information table708 listing origin and arrival airports, and an incident informationtable 712, which may list, without limitation, flight delays, landingdelays, passenger feedback, and/or other incidents.

Referring again to FIG. 4 , one or more elements of excursioncircumstance data may be stored in and/or retrieved from an excursioncircumstance information database 408.

Referring now to FIG. 8 , one or more database tables in excursioncircumstance information database 408 may include, as a non-limitingexample a weather table 800 listing weather conditions at the time ofthe aeronautic excursion. One or more tables may include a temporalcircumstances table 804, which may list a season, time of day, date,time, or other temporal attribute of the aeronautic excursion or one ormore portions thereof. One or more tables may include a geographicalcircumstances table 808 listing one or more geographical circumstancesof an aeronautic excursion as described above.

Referring again to FIG. 4 , one or more elements of excursioncommunication data may be stored in and/or retrieved from an excursioncommunication database 412.

Referring now to FIG. 9 , one or more database tables in excursioncommunication database 412 may include, as a non-limiting example mayinclude a communication log table 900 listing or containing logs ofcommunication between aircraft crew and other entities. One or moretables may include a flight recorder table 904 listing flight recorderinformation. One or more tables may include a navigation communicationtable 908 listing transponder, ADS-B, or other data used to conveynavigation information from and/or to an aircraft.

Referring again to FIG. 4 , one or more elements of sapient payload datamay be stored in and/or retrieved from sapient payload database 416.

Referring now to FIG. 10 , one or more database tables in sapientpayload database 416 may include, as a non-limiting example anenumeration table 1000, which may list an enumeration of sapient payloadas described above. One or more tables may include a weight table 1004,which may list any or all weights of sapient payload, including averageweight per person, total weight, and the like. One or more tables mayinclude a demographics table 1008, which may list any demographicinformation as described above.

Referring again to FIG. 4 , one or more elements of non-sapient payloaddata may be stored in and/or retrieved from a non-sapient payloaddatabase 420.

Referring now to FIG. 11 , one or more database tables in non-sapientpayload database 420 may include, as a non-limiting example, anenumeration table 1100, which may list an enumeration of non-sapientpayload as described above. One or more tables may include a weighttable 1104, which may list any or all weights of non-sapient payload,including average weight per item, total weight, and the like. One ormore tables may include a volume table 1108, which may list total volumeand/or volume per item of non-sapient payload.

Referring again to FIG. 3 , at least a server 104 may be configured toproduce the corpus of mixed payload aeronautic excursion data byproducing a plurality of sapient payload-to-weight entries, each sapientpayload-to-weight entry of the plurality of sapient payload-to-weightentries correlating a number of mixed payloads to a non-sapient payloadweight factor. This may be accomplished, without limitation, byretrieving linked and/or associated data from databases as describedabove in reference to FIG. 4 ; for instance, at least a server 104 mayassemble a training set of corpus of aeronautic excursion data 300 byexecuting a query at one or more databases to retrieve at least a firstexcursion parameter datum and correlated at least a second excursionparameter datum. Alternatively or additionally, data feeds may be usedto acquire aeronautic excursion data entries. For instance, and withoutlimitation, an operator data feed 316 may receive streamed ordiscrete-message data from entities operating aircraft, which mayinclude, without limitation, pre-excursion data, aircraft data,aeronautic excursion circumstance data, aeronautic excursion data,excursion communication data, sapient payload data, and/or non-sapientpayload data for each aeronautic excursion. As a further non-limitingexample, an airport data feed 320 may receive streamed ordiscrete-message data from airports, traffic controllers, or the like;received data may include, without limitation, pre-excursion data,aircraft data, aeronautic excursion circumstance data, aeronauticexcursion data, excursion communication data, sapient payload data,and/or non-sapient payload data for each aeronautic excursion. As anadditional non-limiting example, a weather data feed 324 may providereceive streamed or discrete-message flight circumstances dataconcerning weather; weather data feed 324 may receive information from aweather forecasting and/or reporting service such as, in an non-limitingexample, WEATHER.COM as operated by The Weather Company, LLC of AtlantaGa., and/or weather feeds from the National Oceanographic andAtmospheric Administration (NOAA).

Still referring to FIG. 3 , system 200 may include a feed parsing module328, which may categorize and format data for use in corpus ofaeronautic excursion data 300, creation of training sets included incorpus of aeronautic excursion data 300, and/or for placement indatabases as described above in reference to FIG. 4 . Data in feeds maybe received in a standardized form, including without limitation afixed-length textual field form, a comma-separated value (CSV) form, orin a self-describing format such as without limitation extensible markuplanguage (XML); feed parsing module 328 may be configured to placestandardized fields in associated variables, which may be interconnectedvia references, pointers, or inclusion in common data structures orhierarchies thereof to preserve existing correlations in data asprovided in feeds. Alternatively or additionally, feed parsing module328 may tokenize or otherwise separate and analyze data that is innon-standard formats to sort it into such variables, data structures,and/or hierarchies of data structures. Feed parsing module 328 may beconfigured to link data from disparate feeds together using variablesdata structures, and/or hierarchies of data structures. Linking may beperformed using common data; for instance, where operator data feed 316and airport data feed 320 each provide records pertaining to aparticular identifier of an aeronautic excursion, or to a particular anduniquely identifiable carrier, destination, departure, date, and/or timecombination, feed parsing module 328 may be configured to match thelinking data and combine data concerning the same aeronautic excursionfrom the two feeds together. As a further example, feed parsing module328 may match up weather forecast and/or reporting data from weatherdata feed 324 with data acquired from operator data feed 316 and/orairport data feed 320 by matching date, time, and location informationto link aeronautic excursion data up with circumstances data describingforecasted and actual weather conditions.

With continued reference to FIG. 3 , production of corpus of aeronauticexcursion data 300 may include production of training sets correlatingat least a first aeronautic excursion parameter datum 308 and at least asecond aeronautic excursion parameter datum 312. There may be a singlesuch training set or a plurality thereof. As a non-limiting example, atleast a server 104 may be configured to produce the corpus of mixedpayload aeronautic excursion data by producing a plurality of sapientpayload-to-weight entries, each sapient payload-to-weight entry of theplurality of sapient payload-to-weight entries correlating at least anaeronautic excursion parameter datum to a sapient payload weight datum,where a sapient payload weight datum may include any datum correspondingto a partial or total weight of sapient payload, including withoutlimitation a total weight of sapient payload, an average weight perperson, or the like. As a further non-limiting example, at least aserver 104 may be configured to produce the corpus of aeronauticexcursion data 300 by producing a plurality of sapient payloadenumeration entries, each sapient payload enumeration entry of theplurality of sapient payload enumeration entries correlating at least anaeronautic excursion parameter datum to a sapient payload enumeration onan aeronautic excursion, where a sapient payload enumeration representsa number of persons making up a sapient payload on the aeronauticexcursion; enumeration may further include enumerations by category suchas an enumeration of persons in one passenger class and an enumerationof persons in another passenger class.

Still referring to FIG. 3 , and as a further non-limiting example, atleast a server 104 may be configured to produce the corpus of aeronauticexcursion data 300 by producing a plurality of non-sapientpayload-to-weight entries, each non-sapient payload-to-weight entry ofthe plurality of sapient payload-to-weight entries correlating at leastan aeronautic excursion parameter datum to a non-sapient payload weightdatum, where a non-sapient payload weight datum may include any datumcorresponding to a partial or total weight of non-sapient payload,including without limitation a total weight of non-sapient payload, anaverage weight per item of non-sapient payload, or the like. As afurther non-limiting example, at least a server 104 may be configured toproduce the corpus of aeronautic excursion data 300 by producing aplurality of non-sapient payload-to-volume entries, each non-sapientpayload-to-volume entry of the plurality of sapient payload-to-weightentries correlating at least an aeronautic excursion parameter datum tonon-sapient payload volume datum, where a non-sapient payload volumedatum may include any datum corresponding to a partial or total volumeof non-sapient payload, including without limitation a total volume ofnon-sapient payload, a total volume of storage space occupied bynon-sapient payload, a total volume of a specific compartment or storagearea occupied by non-sapient payload, an average volume per item ofnon-sapient payload, or the like. This data may, as a non-limitingexample, be tracking a likelihood that a projected enumeration mayincrease or decrease, for instance because of persons switching from oneaeronautic excursion to another, being reassigned to an aeronauticexcursion owing to weather delays, or the like.

With continued reference to FIG. 3 , at least a server 104 may beconfigured to produce the corpus of aeronautic excursion data 300 byproducing a plurality of fuel use entries, each fuel use entry of theplurality of fuel use entries correlating at least an aeronauticexcursion parameter datum to a fuel use datum, where a fuel consumptiondatum is an element of data that describes a rate or quantity of fuelconsumption. As a non-limiting example, training data may correlate fuelconsumption rates to weather conditions, particular flight paths,particular airports (such as, without limitation, airports more likelyto impose significant landing delays), or the like. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various additional training sets containing various additionalcorrelations which may be used and/or produced consistently with thisdisclosure.

Continuing to refer to FIG. 3 , at least a server 104 is configured toreceive at least an aeronautic excursion parameter regarding a futureaeronautic excursion. At least an aeronautic excursion parameter may bereceived by any suitable means, including without limitation receipt viaoperator data feed 316, airport data feed 320, and/or weather data feed324, or a combination thereof, as described above. As a non-limitingexample, at least an aeronautic excursion parameter may include anidentifier of an aeronautic excursion, a departure location, a projectedtime of departure, a destination, and the like as provided by an airportdata feed 320. An operator data feed 316 may indicate a currentenumeration of sapient payload and may also indicate a current level ofnon-sapient payload, including without limitation luggage and/orcarry-on items that persons in the projected sapient payload haveindicated they will bring. A weather data feed 324 may provide currentweather information as well as forecast data indicating likely weatherconditions at the time of the aeronautic excursion. At least a server104 may combine and/or categories this information, for instance usingfeed parsing module 328, so as to provide it to further modules asdescribed below. In an embodiment, data from at least an aeronauticexcursion parameter may be added to one or more databases as describedabove in reference to FIG. 4 , and/or to corpus of aeronautic excursiondata 300 and/or training sets incorporated therein. At least anaeronautic excursion parameter may be received a single time or may beupdated via periodic and/or streamed receipt of additional data viaairport data feed 320, operator data feed 316, weather data feed 324, orthe like.

In an embodiment, and still referring to FIG. 3 , at least an aeronauticexcursion parameter of a future aeronautic excursion may include afreight request; as used in this disclosure, a freight request is anelement of data assigning at least an item of non-sapient payload thatis not associated with a person of sapient payload to storage space inan aircraft that is undertraining the future aeronautic excursion. Inother words, a freight request assigns at least an item of non-sapientpayload that is not at least an item of luggage, carry-on luggage, orpersonal effects of a passenger; such an item may be a part of shipmentof goods or materials to be transported using the aircraft during thefuture aeronautic excursion. In an embodiment, a user or entity mayenter a freight request via a graphical user interface 332; forinstance, a user may have an item of non-sapient payload that userwishes to convey from a first geographical location to a secondgeographical location. Continuing the above non-limiting example, usermay locate, in a graphical user interface 332 operating on at least aserver 104, one or more aeronautic excursions that are scheduled tooccur from the first geographical location to the second geographicallocation; at least a server 104 may, as a non-limiting example, displaysuch geographical excursions using data acquired from at least anaeronautic excursion parameter as described above. Graphical userinterface 332 may enable users to search for aeronautic excursions bydate, time, origin, and/or destination; persons skilled in the art, uponviewing the entirety of this disclosure, will be aware of various waysin which graphical user interface 332 may permit a user to search and/orbrowse through at least an aeronautic excursion parameter to locate adesired aeronautic excursion. In an embodiment, graphical user interface332 may display a current non-sapient payload capacity, which mayrepresent an amount of storage space and/or weight available on anaircraft undertaking a given aeronautic excursion; non-sapient payloadcapacity may be determined as described in further detail below.Non-sapient payload capacity may include a volume of space available ina particular compartment of an aircraft, such as a luggage compartment;available dimensions of a luggage compartment, and/or of space therein,may also be included.

In an embodiment, and continuing to refer to FIG. 3 , a user may enter avolume and/or weight of non-sapient payload that the user wishes tosend; this may be entered, without limitation, using fields such asdrop-down lists or text fields, which may include elements prepopulatingfields with a range of permissible values based on current non-sapientpayload capacity, and/or event handlers and/or scripts that enforcelimits based on current non-sapient payload capacity. Where at least afreight request is entered, the at least a freight request may becombined with other data of at least an aeronautic excursion parameter;in other words subsequent and/or iterative determinations of currentnon-sapient payload capacity may account for any entered freightrequests, which may represent an additional limit on available volumeand/or weight for more sapient and/or non-sapient payload elements.

Still referring to FIG. 3 , system 200 includes a capacity estimationartificial intelligence module 220 operating on the at least a server104. Capacity estimation artificial intelligence module 220 may includeany suitable hardware or software module. Capacity estimation artificialintelligence module 220 is designed and configured to generate an excessnon-sapient payload storage estimation as a function of the corpus ofaeronautic excursion data 300 and the at least an aeronautic excursionparameter. Capacity estimation artificial intelligence module 220 mayinclude one or more learners which engage in machine learning, deeplearning, or similar algorithms to develop models and/or heuristicsusable to generate excess non-sapient storage estimation, which mayinclude one or more calculations usable to determine current non-sapientpayload capacity as defined above. In the following exemplaryillustrations, one or more such learners are described for exemplarypurposes only; one or more learners as descried above may be combined ina single module with one or more other learners and/or used to produce acombined model with one or more other learners. A given learner may beinstantiated by any hardware and/or software instructions producingfunctionality ascribed to the learner as described below, whether in adiscrete module or in a module combining some or all of suchfunctionality with other functionality described herein or compatibletherewith. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various alternative approaches toinstantiating the below-described learners and/or functionality thereof,as dictated by various stylistic and/or practical choices, benefits,and/or constraints.

In an embodiment, capacity estimation artificial intelligence module 220may include, in the sense described above, a weight capacity learner336, the weight capacity learner 336 designed and configured todetermine a non-sapient payload weight capacity a function of the corpusof aeronautic excursion data 300 and the at least an aeronauticexcursion parameter. Weight capacity learner 336 may be designed andconfigured to generate outputs using machine learning processes. Amachine learning process is a process that automatedly uses a body ofdata known as “training data” and/or a “training set” to generate analgorithm that will be performed by a computing device/module to produceoutputs given data provided as inputs; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage.

Still referring to FIG. 3 , weight capacity learner 336 may be designedand configured to generate at least an output by creating at least aweight capacity model relating aeronautic excursion parameter data toweight capacity using the corpus of aeronautic excursion data 300 andgenerating the at least an output using the weight capacity model; atleast a weight capacity model may include one or more models thatdetermine a mathematical relationship between aeronautic excursionparameter data and weight capacity. Such models may include withoutlimitation model developed using linear regression models. Linearregression models may include ordinary least squares regression, whichaims to minimize the square of the difference between predicted outcomesand actual outcomes according to an appropriate norm for measuring sucha difference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 3 , machine-learning algorithm used togenerate weight capacity model may include, without limitation, lineardiscriminant analysis. Machine-learning algorithm may include quadraticdiscriminate analysis. Machine-learning algorithms may include kernelridge regression. Machine-learning algorithms may include support vectormachines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 3 , weight capacity learner 336 may generate aweight capacity output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using corpus ofaeronautic excursion data 300; the trained network may then be used toapply detected relationships between elements of aeronautic excursionparameter data and weight capacity.

Referring now to FIG. 12 , machine-learning algorithms used by weightcapacity learner 336 may include supervised machine-learning algorithms,which may, as a non-limiting example be executed using a supervisedlearning module 1200 executing on at least a server 104 and/or onanother computing device in communication with at least a server 104,which may include any hardware or software module. Supervised machinelearning algorithms, as defined herein, include algorithms that receivea training set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useaeronautic excursion parameters as inputs, weight capacity as outputs,and a scoring function representing a desired form of relationship to bedetected between aeronautic excursion parameters and weight capacity;scoring function may, for instance, seek to maximize the probabilitythat a given element of aeronautic excursion parameter data and/orcombination of aeronautic excursion parameters is associated with agiven weight capacity to minimize the probability that a given elementof aeronautic excursion parameter data and/or combination of elements ofaeronautic excursion parameter data is not associated with a givenweight capacity. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in corpus of aeronauticexcursion data 300. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of supervised machine learning algorithms that may be used todetermine relation between aeronautic excursion parameters and weightcapacity. In an embodiment, one or more supervised machine-learningalgorithms may be restricted to a particular domain; for instance, asupervised machine-learning process may be performed with respect to agiven set of parameters and/or categories of parameters that have beensuspected to be related to a given set of weight capacity. Domainrestrictions may be suggested by experts and/or deduced from knownpurposes for particular evaluations and/or known tests used to evaluateweight capacity. Additional supervised learning processes may beperformed without domain restrictions to detect, for instance,previously unknown and/or unsuspected relationships between aeronauticexcursion parameters and weight capacity.

Still referring to FIG. 12 , machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 1204 executingon at least a server 104 and/or on another computing device incommunication with at least a server 104, which may include any hardwareor software module. An unsupervised machine-learning process, as usedherein, is a process that derives inferences in datasets without regardto labels; as a result, an unsupervised machine-learning process may befree to discover any structure, relationship, and/or correlationprovided in the data. For instance, and without limitation, weightcapacity learner 336 and/or at least a server 104 may perform anunsupervised machine learning process on corpus of aeronautic excursiondata 300, which may cluster data of corpus of aeronautic excursion data300 according to detected relationships between elements of the corpusof aeronautic excursion data 300, including without limitationcorrelations of elements of aeronautic excursion parameter data to eachother and correlations of weight capacity data to each other; suchrelations may then be combined with supervised machine learning resultsto add new criteria for weight capacity learner 336 to apply in relatingaeronautic excursion parameter data to weight capacity. Continuing theexample a close correlation between first element of aeronauticexcursion parameter data and second element of aeronautic excursionparameter data may indicate that the second element is also a goodpredictor for the weight capacity; second element may be included in anew supervised process to derive a relationship or may be used as asynonym or proxy for the first aeronautic excursion parameter by weightcapacity learner 336.

Still referring to FIG. 12 , at least a server 104 and/or weightcapacity learner 336 may detect further significant categories ofaeronautic parameters, relationships of such categories to weightcapacity, and/or categories of weight capacity using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above. In an embodiment, as additional data isadded to system 200, weight capacity learner 336 and/or at least aserver 104 may continuously or iteratively perform unsupervisedmachine-learning processes to detect relationships between differentelements of the added and/or overall data; in an embodiment, this mayenable system 200 to use detected relationships to discover newcorrelations between aeronautic excursion parameters and weightcapacity. Use of unsupervised learning may greatly enhance the accuracyand detail with which system may estimate weight capacity.

Still referring to FIG. 12 , weight capacity learner 336 may produce amachine-learning model 1208. A “machine-learning model,” as used herein,is a mathematical representation of a relationship between inputs andoutputs, as generated using any machine-learning process includingwithout limitation any process as described above, and stored in memory;an input is submitted to a machine-learning model once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model may be generated by creating an artificial neuralnetwork, such as a convolutional neural network comprising an inputlayer of nodes, one or more intermediate layers, and an output layer ofnodes. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Alternatively or additionally, and still referring to FIG. 12 , weightcapacity learner 336 may be designed and configured to generate at leastan output by executing a lazy learning process as a function of thecorpus of aeronautic excursion data 300 and the at least a physiologicaltest sample; lazy learning processes may be performed by a lazy learningmodule 1212 executing on at least a server 104 and/or on anothercomputing device in communication with at least a server 104, which mayinclude any hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” orheuristic of weight capacity associated with at least an aeronauticexcursion parameter, using corpus of aeronautic excursion data 300.Heuristic may include selecting some number of highest-rankingassociations and/or weight capacity. Weight capacity learner 336 mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure.

In an embodiment, and still referring to FIG. 12 , weight capacitylearner 336 may operate by determining a current weight limit for anaeronautic excursion; current weight limit may be based on a fuel usedatum representing a likely rate or total volume of fuel use on theaeronautic excursion based on current overall weight of the aircraft, asdetermined using aircraft data, an estimated weight of sapient payload,and an estimated weight of non-sapient payload. Each of these estimatesmay be calculated using any machine-learning algorithm as describedabove, in combination with training sets as described above. Forinstance, and without limitation, a training set correlating at least anaeronautic excursion parameter to a sapient payload weight datum may beused to determine an estimated sapient payload weight, by generating amachine-learning model such as a weight-capacity model and/or via a lazylearning process as described above; this may be performed, in addition,by combination with machine-learning processes estimating a likelyenumeration of sapient payload using sapient payload enumeration entriescorrelating at least an aeronautic excursion parameter datum to asapient payload enumeration on an aeronautic excursion. As a furtherexample, a training set correlating at least an aeronautic excursionparameter to a non-sapient payload weight datum may be used to determinean estimated non-sapient payload weight, by generating amachine-learning model such as a weight-capacity model and/or via a lazylearning process as described above. Calculation of non-sapient weightcapacity may be performed by comparison of two or more of theabove-described values, including by subtraction of current overallweight from an overall weight capacity derived as described above todetermine what excess weight capacity remains. One or more bufferamounts may be included in determination of excess non-sapient weightcapacity; for instance, an error function as described above mayindicate a degree of uncertainty in predicting non-sapient weightcapacity, and calculation of non-sapient weight capacity may beperformed by subtracting from the estimated non-sapient weight capacityan amount of weight equal or proportional to the degree of uncertaintyas described above.

Referring again to FIG. 3 , capacity estimation artificial intelligencemodule 220 may include a volume capacity learner 340, the volumecapacity learner 340 designed and configured to determine a non-sapientpayload volume capacity a function of the corpus of aeronautic excursiondata 300 and the at least an aeronautic excursion parameter; this may beaccomplished using any machine-learning algorithms as described aboveregarding weight capacity learner 336, or any combination thereof. As anonlimiting example, volume capacity learner 340 may operate, withoutlimitation, using non-sapient payload volume entries as described abovein combination with machine-learning processes to generate an estimatednon-sapient payload volume capacity using the at least an aeronauticexcursion parameter.

Referring now to 13, machine-learning algorithms used by volume capacitylearner 340 may include supervised machine-learning algorithms, whichmay, as a non-limiting example be executed using a supervised learningmodule 1300 executing on at least a server 104 and/or on anothercomputing device in communication with at least a server 104, which mayinclude any hardware or software module. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useaeronautic excursion parameters as inputs, volume capacity as outputs,and a scoring function representing a desired form of relationship to bedetected between aeronautic excursion parameters and volume capacity;scoring function may, for instance, seek to maximize the probabilitythat a given element of aeronautic excursion parameter data and/orcombination of aeronautic excursion parameters is associated with agiven volume capacity to minimize the probability that a given elementof aeronautic excursion parameter data and/or combination of elements ofaeronautic excursion parameter data is not associated with a givenvolume capacity. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in corpus of aeronauticexcursion data 300. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of supervised machine learning algorithms that may be used todetermine relation between aeronautic excursion parameters and volumecapacity. In an embodiment, one or more supervised machine-learningalgorithms may be restricted to a particular domain; for instance, asupervised machine-learning process may be performed with respect to agiven set of parameters and/or categories of parameters that have beensuspected to be related to a given set of volume capacity. Domainrestrictions may be suggested by experts and/or deduced from knownpurposes for particular evaluations and/or known tests used to evaluatevolume capacity. Additional supervised learning processes may beperformed without domain restrictions to detect, for instance,previously unknown and/or unsuspected relationships between aeronauticexcursion parameters and volume capacity.

Still referring to 13, machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 1304 executingon at least a server 104 and/or on another computing device incommunication with at least a server 104, which may include any hardwareor software module. An unsupervised machine-learning process, as usedherein, is a process that derives inferences in datasets without regardto labels; as a result, an unsupervised machine-learning process may befree to discover any structure, relationship, and/or correlationprovided in the data. For instance, and without limitation, volumecapacity learner 340 and/or at least a server 104 may perform anunsupervised machine learning process on corpus of aeronautic excursiondata 300, which may cluster data of corpus of aeronautic excursion data300 according to detected relationships between elements of the corpusof aeronautic excursion data 300, including without limitationcorrelations of elements of aeronautic excursion parameter data to eachother and correlations of volume capacity data to each other; suchrelations may then be combined with supervised machine learning resultsto add new criteria for volume capacity learner 340 to apply in relatingaeronautic excursion parameter data to volume capacity. Continuing theexample a close correlation between first element of aeronauticexcursion parameter data and second element of aeronautic excursionparameter data may indicate that the second element is also a goodpredictor for the volume capacity; second element may be included in anew supervised process to derive a relationship or may be used as asynonym or proxy for the first aeronautic excursion parameter by volumecapacity learner 340.

Still referring to 13, at least a server 104 and/or volume capacitylearner 340 may detect further significant categories of aeronauticparameters, relationships of such categories to volume capacity, and/orcategories of volume capacity using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above. In an embodiment, as additional data is added to system200, volume capacity learner 340 and/or at least a server 104 maycontinuously or iteratively perform unsupervised machine-learningprocesses to detect relationships between different elements of theadded and/or overall data; in an embodiment, this may enable system 200to use detected relationships to discover new correlations betweenaeronautic excursion parameters and volume capacity. Use of unsupervisedlearning may greatly enhance the accuracy and detail with which systemmay estimate volume capacity.

Still referring to 13, volume capacity learner 340 may produce amachine-learning model 1220; alternatively or additionally volumecapacity learner 340 may alternatively or additionally be designed andconfigured to generate at least an output by executing a lazy learningprocess as a function of the corpus of aeronautic excursion data 300 andthe at least a physiological test sample; lazy learning processes may beperformed by a lazy learning module 1308 executing on at least a server104 and/or on another computing device in communication with at least aserver 104, which may include any hardware or software module. Alazy-learning process and/or protocol, which may alternatively bereferred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover a “first guess” or heuristic of volume capacityassociated with at least an aeronautic excursion parameter, using corpusof aeronautic excursion data 300. Heuristic may include selecting somenumber of highest-ranking associations and/or volume capacity. Volumecapacity learner 340 may alternatively or additionally implement anysuitable “lazy learning” algorithm, including without limitation aK-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various lazy-learning algorithms that maybe applied to generate outputs as described in this disclosure.

With continued reference to 13, volume capacity learner 340 may operateby determining a current volume limit for an aeronautic excursion;current volume limit may be based on current overall volume in theaircraft and/or in one or more storage compartments thereof, asdetermined using aircraft data and an estimated volume of non-sapientpayload. Estimates may be calculated using any machine-learningalgorithm as described above, in combination with training sets asdescribed above. For instance, and without limitation, a training setcorrelating at least an aeronautic excursion parameter to a non-sapientpayload volume datum may be used to determine an estimated non-sapientpayload volume, by generating a machine-learning model such as avolume-capacity model and/or via a lazy learning process as describedabove. Calculation of non-sapient volume capacity may be performed bycomparison of two or more of the above-described values, including bysubtraction of current overall volume from an overall volume capacityderived as described above to determine what excess volume capacityremains. One or more buffer amounts may be included in determination ofexcess non-sapient volume capacity; for instance, an error function asdescribed above may indicate a degree of uncertainty in predictingnon-sapient volume capacity, and calculation of non-sapient volumecapacity may be performed by subtracting from the estimated non-sapientvolume capacity an amount of volume equal or proportional to the degreeof uncertainty as described above.

Referring again to FIG. 3 , capacity estimation artificial intelligencemodule 220 may include a sapient payload estimator 344, the sapientpayload estimator 344 designed and configured to determine a sapientpayload as a function of the corpus of aeronautic excursion data 300 andthe at least an aeronautic excursion parameter; this may be accomplishedusing any machine-learning algorithms as described above regardingweight capacity learner 336, or any combination thereof. For instance,and without limitation, sapient payload estimator 344 may perform one ormore machine-learning algorithms using training data containing at leasta sapient payload enumeration entry of the plurality of sapient payloadenumeration entries correlating at least an aeronautic excursionparameter datum to a sapient payload enumeration on an aeronauticexcursion as described above.

Referring now to FIG. 14 , machine-learning algorithms used by sapientpayload estimator 344 may include supervised machine-learningalgorithms, which may, as a non-limiting example be executed using asupervised learning module 1400 executing on at least a server 104and/or on another computing device in communication with at least aserver 104, which may include any hardware or software module.Supervised machine learning algorithms, as defined herein, includealgorithms that receive a training set relating a number of inputs to anumber of outputs, and seek to find one or more mathematical relationsrelating inputs to outputs, where each of the one or more mathematicalrelations is optimal according to some criterion specified to thealgorithm using some scoring function. For instance, a supervisedlearning algorithm may use aeronautic excursion parameters as inputs,sapient payload and/or sapient payload enumerations as outputs, and ascoring function representing a desired form of relationship to bedetected between aeronautic excursion parameters and sapient payloadsand/or enumerations; scoring function may, for instance, seek tomaximize the probability that a given element of aeronautic excursionparameter data and/or combination of aeronautic excursion parameters isassociated with a given sapient payload and/or enumeration, and/or tominimize the probability that a given element of aeronautic excursionparameter data and/or combination of elements of aeronautic excursionparameter data is not associated with a given sapient payload and/orenumeration. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in corpus of aeronauticexcursion data 300. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of supervised machine learning algorithms that may be used todetermine relation between aeronautic excursion parameters and sapientpayloads and/or enumerations. In an embodiment, one or more supervisedmachine-learning algorithms may be restricted to a particular domain;for instance, a supervised machine-learning process may be performedwith respect to a given set of parameters and/or categories ofparameters that have been suspected to be related to a given set ofsapient payloads and/or enumerations. Additional supervised learningprocesses may be performed without domain restrictions to detect, forinstance, previously unknown and/or unsuspected relationships betweenaeronautic excursion parameters and sapient payloads and/orenumerations.

Still referring to 14, machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 1404 executingon at least a server 104 and/or on another computing device incommunication with at least a server 104, which may include any hardwareor software module. An unsupervised machine-learning process, as usedherein, is a process that derives inferences in datasets without regardto labels; as a result, an unsupervised machine-learning process may befree to discover any structure, relationship, and/or correlationprovided in the data. For instance, and without limitation, sapientpayload estimator 344 and/or at least a server 104 may perform anunsupervised machine learning process on corpus of aeronautic excursiondata 300, which may cluster data of corpus of aeronautic excursion data300 according to detected relationships between elements of the corpusof aeronautic excursion data 300, including without limitationcorrelations of elements of aeronautic excursion parameter data to eachother and correlations of sapient payload data to each other; suchrelations may then be combined with supervised machine learning resultsto add new criteria for sapient payload estimator 344 to apply inrelating aeronautic excursion parameter data to sapient payloads and/orenumerations. Continuing the example a close correlation between firstelement of aeronautic excursion parameter data and second element ofaeronautic excursion parameter data may indicate that the second elementis also a good predictor for a sapient payload and/or enumeration;second element may be included in a new supervised process to derive arelationship or may be used as a synonym or proxy for the firstaeronautic excursion parameter by sapient payload estimator 344.

Still referring to 14, at least a server 104 and/or sapient payloadestimator 344 may detect further significant categories of aeronauticparameters, relationships of such categories to sapient payloads and/orenumerations, and/or categories of sapient payloads and/or enumerationsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described above. In anembodiment, as additional data is added to system 200, sapient payloadestimator 344 and/or at least a server 104 may continuously oriteratively perform unsupervised machine-learning processes to detectrelationships between different elements of the added and/or overalldata; in an embodiment, this may enable system 200 to use detectedrelationships to discover new correlations between aeronautic excursionparameters and sapient payload. Use of unsupervised learning may greatlyenhance the accuracy and detail with which system may estimate sapientpayload.

Still referring to 14, sapient payload estimator 344 may produce amachine-learning model 1408; alternatively or additionally sapientpayload estimator 344 may alternatively or additionally be designed andconfigured to generate at least an output by executing a lazy learningprocess as a function of the corpus of aeronautic excursion data 300 andthe at least a physiological test sample; lazy learning processes may beperformed by a lazy learning module 1412 executing on at least a server104 and/or on another computing device in communication with at least aserver 104, which may include any hardware or software module. Alazy-learning process and/or protocol, which may alternatively bereferred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover a “first guess” or heuristic of sapient payloadassociated with at least an aeronautic excursion parameter, using corpusof aeronautic excursion data 300. Heuristic may include selecting somenumber of highest-ranking associations and/or sapient payload. Sapientpayload estimator 344 may alternatively or additionally implement anysuitable “lazy learning” algorithm, including without limitation aK-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various lazy-learning algorithms that maybe applied to generate outputs as described in this disclosure,including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

Referring again to FIG. 3 , capacity estimation artificial intelligencemay include a fuel use learner 348 configured to relate at least anaeronautic excursion parameter datum to a fuel use datum as a functionof the corpus of aeronautic excursion data 300; this may be accomplishedusing any machine-learning algorithms as described above regardingweight capacity learner 336, or any combination thereof. For instance,and without limitation, fuel user learner may perform one or moremachine-learning algorithms using training data containing a pluralityof fuel use entries, each fuel use entry of the plurality of fuel useentries correlating at least an aeronautic excursion parameter datum toa fuel use datum.

Referring now to 15, machine-learning algorithms used by fuel uselearner 348 may include supervised machine-learning algorithms, whichmay, as a non-limiting example be executed using a supervised learningmodule 1500 executing on at least a server 104 and/or on anothercomputing device in communication with at least a server 104, which mayinclude any hardware or software module. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useaeronautic excursion parameters as inputs, fuel use data as outputs, anda scoring function representing a desired form of relationship to bedetected between aeronautic excursion parameters and fuel use; scoringfunction may, for instance, seek to maximize the probability that agiven element of aeronautic excursion parameter data and/or combinationof aeronautic excursion parameters is associated with a given rate oroverall volume of fuel use to minimize the probability that a givenelement of aeronautic excursion parameter data and/or combination ofelements of aeronautic excursion parameter data is not associated with agiven rate or volume of fuel use. Scoring function may be expressed as arisk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided in corpusof aeronautic excursion data 300. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of supervised machine learning algorithms that maybe used to determine relation between aeronautic excursion parametersand fuel use data. In an embodiment, one or more supervisedmachine-learning algorithms may be restricted to a particular domain;for instance, a supervised machine-learning process may be performedwith respect to a given set of parameters and/or categories ofparameters that have been suspected to be related to a given set of fueluse. Additional supervised learning processes may be performed withoutdomain restrictions to detect, for instance, previously unknown and/orunsuspected relationships between aeronautic excursion parameters andfuel use data.

Still referring to 15, machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 1504 executingon at least a server 104 and/or on another computing device incommunication with at least a server 104, which may include any hardwareor software module. An unsupervised machine-learning process, as usedherein, is a process that derives inferences in datasets without regardto labels; as a result, an unsupervised machine-learning process may befree to discover any structure, relationship, and/or correlationprovided in the data. For instance, and without limitation, fuel uselearner 348 and/or at least a server 104 may perform an unsupervisedmachine learning process on corpus of aeronautic excursion data 300,which may cluster data of corpus of aeronautic excursion data 300according to detected relationships between elements of the corpus ofaeronautic excursion data 300, including without limitation correlationsof elements of aeronautic excursion parameter data to each other andcorrelations of fuel use data to each other; such relations may then becombined with supervised machine learning results to add new criteriafor fuel use learner 348 to apply in relating aeronautic excursionparameter data to fuel use data. Continuing the example a closecorrelation between first element of aeronautic excursion parameter dataand second element of aeronautic excursion parameter data may indicatethat the second element is also a good predictor for the fuel use;second element may be included in a new supervised process to derive arelationship or may be used as a synonym or proxy for the firstaeronautic excursion parameter by fuel use learner 348.

Still referring to 15, at least a server 104 and/or fuel use learner 348may detect further significant categories of aeronautic parameters,relationships of such categories to fuel use data, and/or categories offuel use data using machine-learning processes, including withoutlimitation unsupervised machine-learning processes as described above.In an embodiment, as additional data is added to system 200, fuel uselearner 348 and/or at least a server 104 may continuously or iterativelyperform unsupervised machine-learning processes to detect relationshipsbetween different elements of the added and/or overall data; in anembodiment, this may enable system 200 to use detected relationships todiscover new correlations between aeronautic excursion parameters andfuel use data. Use of unsupervised learning may greatly enhance theaccuracy and detail with which system may estimate fuel use data.

Still referring to 15, fuel use learner 348 may produce amachine-learning model 1508; alternatively or additionally fuel uselearner 348 may alternatively or additionally be designed and configuredto generate at least an output by executing a lazy learning process as afunction of the corpus of aeronautic excursion data 300 and the at leasta physiological test sample; lazy learning processes may be performed bya lazy learning module 1512 executing on at least a server 104 and/or onanother computing device in communication with at least a server 104,which may include any hardware or software module. A lazy-learningprocess and/or protocol, which may alternatively be referred to as a“lazy loading” or “call-when-needed” process and/or protocol, may be aprocess whereby machine learning is conducted upon receipt of an inputto be converted to an output, by combining the input and training set toderive the algorithm to be used to produce the output on demand. Forinstance, an initial set of simulations may be performed to cover a“first guess” or heuristic of fuel use associated with at least anaeronautic excursion parameter, using corpus of aeronautic excursiondata 300. Heuristic may include selecting some number of highest-rankingassociations and/or fuel use. Fuel use learner 348 may alternatively oradditionally implement any suitable “lazy learning” algorithm, includingwithout limitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

Referring again to FIG. 3 , at least a server 104 may be designed and/orconfigured to output an excess non-sapient payload storage estimationbased on the at least an aeronautic excursion parameter. Outputting anexcess non-sapient payload storage estimation may include, withoutlimitation directly providing the excess non-sapient payload storageestimation to a user and/or remote device (not shown); provision to auser may be accomplished, without limitation, via a graphical userinterface 332 as described above. Outputting an excess non-sapientpayload storage estimation may also be performed by using the excessnon-sapient payload storage estimation to enforce limits on permittedand/or accepted freight requests as described above.

Referring now to 15, an exemplary embodiment of an artificialintelligence method 2480 of estimating excess non-sapient payloadcapacity on mixed-payload aeronautic excursions is illustrated. At step1605, at least a server 104 produces a corpus of mixed payloadaeronautic excursion data including a plurality of aeronautic excursiondata entries, where each aeronautic excursion data entry 304 correlatesat least a first aeronautic excursion parameter datum 308 and at least asecond aeronautic excursion parameter datum 312; this may beaccomplished, without limitation, as described above in reference toFIGS. 1-16 . As a non-limiting example, producing corpus of aeronauticexcursion data 300 may include producing a plurality of sapientpayload-to-weight entries, each sapient payload-to-weight entry of theplurality of sapient payload-to-weight entries correlating at least anaeronautic excursion parameter datum to a sapient payload weight datum,for instance as described above in reference to FIGS. 1-16 . As afurther non-limiting example, producing the corpus of aeronauticexcursion data 300 may include producing a plurality of sapient payloadenumeration entries, each sapient payload enumeration entry of theplurality of sapient payload enumeration entries correlating at least anaeronautic excursion parameter datum to a sapient payload enumeration onan aeronautic excursion, for instance as described above in reference toFIGS. 1-16 . As an additional non-limiting example, producing corpus ofaeronautic excursion data 300 may include producing a plurality ofsapient payload-to-weight entries, each sapient payload-to-weight entryof the plurality of non-sapient payload-to-weight entries correlating atleast an aeronautic excursion parameter datum to a non-sapient payloadweight datum, for instance as described above in reference to FIGS. 1-16. As another example, producing corpus of aeronautic excursion data 300may include producing a plurality of non-sapient payload volume entries,each non-sapient volume entry of the plurality of sapientpayload-to-weight entries correlating at least an aeronautic excursionparameter datum to non-sapient payload volume datum, for instance asdescribed above in reference to FIGS. 1-16 . As a further non-limitingexample, producing corpus of aeronautic excursion data 300 may includeproducing a plurality of fuel use entries, each fuel use entry of theplurality of fuel use entries correlating at least an aeronauticexcursion parameter datum to a fuel use datum, for instance as describedabove in reference to FIGS. 1-13 .

At step 1610, and still referring to 16, at least a server 104 receivesat least an aeronautic excursion parameter regarding at least a futureaeronautic excursion. This may be accomplished, without limitation, asdescribed above in reference to FIGS. 1-16 ; for instance, and withoutlimitation, receiving at least an aeronautic excursion parameter mayinclude receiving the at least an aeronautic excursion parameter from anaeronautic vehicle operator, which may be accomplished via an operatordata feed 316 as described above. As a further non-limiting example,receiving at least an aeronautic excursion parameter from an airport,which may be accomplished via an airport data feed 320 as describedabove. As an additional non-limiting example, receiving at least anaeronautic excursion parameter may include receiving the at least anaeronautic excursion parameter from a weather forecasting apparatus,such as via a weather data feed 324 as described above. As a furthernon-limiting example, receiving at least an aeronautic excursionparameter may include receiving at least a freight-use request; this maybe accomplished as described above in reference to FIGS. 1-16 ,including without limitation via a graphical user interface 332.

At step 1615, and continuing to refer to 15, a capacity estimationartificial intelligence module 220 operating on the at least a server104 generates an excess non-sapient payload storage estimation as afunction of the corpus of aeronautic excursion data 300 and the at leastan aeronautic excursion parameter; this may be accomplished, withoutlimitation, as described above in reference to FIGS. 1-16 . For instanceand without limitation, where at least a server 104 receives at least afreight-use request, generating excess non-sapient payload storageestimation may include modifying the at least an excess non-sapientpayload storage estimation based on the freight-use request.

Still referring to 16, in an embodiment, at least a future aeronauticexcursion may include a first excursion section from a first airport toa second airport and a second excursion section from a second airport toa third airport; for instance, and without limitation, at least a futureaeronautic excursion may have a first “leg” to a first destinationoutputting the at least an excess non-sapient payload storage estimationfurther comprises outputting a first estimation for the first excursionsection and a second estimation for the second excursion section.Capacity estimation artificial intelligence module 220 may generate eachexcess non-sapient payload storage estimation for each excursion sectionusing any method or method steps as described above for generation of anexcess non-sapient payload storage estimation.

At step 1600, and still referring to 16, at least a server 104 outputsat least an excess non-sapient payload storage estimation based on theat least an aeronautic excursion parameter; this may be implemented asdescribed above in reference to FIGS. 1-16 . In an embodiment, at leasta server 104 receives at least a freight-use request and modifies the atleast an excess non-sapient payload storage estimation based on thefreight-use request; this may be implemented as described above inreference to FIGS. 1-16 . As a non-limiting example, where the at leasta future aeronautic excursion further includes a first excursion sectionfrom a first airport to a second airport and a second excursion sectionfrom a second airport to a third airport, outputting the at least anexcess non-sapient payload storage estimation may include outputting afirst estimation for the first excursion section and a second estimationfor the second excursion section; at least a freight-use request mayinclude a first freight-use request corresponding to the first excursionsection and a second freight-use request corresponding to the secondexcursion section, where each of the first freight-use request and thesecond freight-use request may be entered and/or processed as describedabove for entering a freight-use request. As a further non-limitingexample, where receiving the at least an aeronautic excursion parameterregarding at least a future aeronautic excursion further includesreceiving at least a first aeronautic excursion parameter regarding atleast a first future aeronautic excursion and receiving at least asecond aeronautic excursion parameter regarding at least a second futureaeronautic excursion, outputting the at least an excess non-sapientpayload storage estimation may include outputting at least a firstexcess non-sapient payload storage estimation as a function of the atleast a first aeronautic excursion parameter and outputting at least asecond excess non-sapient payload estimation as a function of the atleast a second aeronautic excursion parameter; at least a server 104 mayreceive at least a freight-use request including at least a firstfreight-use request for the at least a first future aeronautic excursionand at least a second freight-use request for the at least a secondfuture aeronautic excursion.

Turning now to FIG. 16 , a method 1600 of method of selection ofphysical asset transfer paths using mixed-payload aeronautic excursions.At step 1605, at least a server 104 receives an initial location, aterminal location, and a description of at least an element ofnon-sapient payload; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-16 . At step 1610, at least aserver 104 identifies at least an aeronautic path from the initiallocation to the terminal location and a plurality of aeronauticexcursions traversing the at least an aeronautic path; this may beimplemented, without limitation, as described above in reference toFIGS. 1-16 . At step 1615, at least a server 104 generates a pluralityexcess non-sapient payload storage estimations corresponding to theplurality of aeronautic excursions; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-16 . At step1620, at least a server 104 selects an aeronautic excursion of theplurality of aeronautic excursions based on the plurality of excessnon-sapient payload storage estimations; this may be implemented,without limitation, as described above in reference to FIGS. 1-16 .

Referring now to FIG. 17 , an exemplary embodiment of a machine-learningmodule 1700 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 1704 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 1708 given data provided as inputs1712; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language.

Still referring to FIG. 17 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 1704 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 1704 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 1704 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 1704 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 1704 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 1704 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data1704 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively, or additionally, and continuing to refer to FIG. 17 ,training data 1704 may include one or more elements that are notcategorized; that is, training data 1704 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 1704 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 1704 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 1704 used by machine-learning module 1700 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 17 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 1716. Training data classifier 1716 may include a“classifier,” which as used in this disclosure is a machine-learningmodel as defined below, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts of inputs into categories or bins of data, outputting thecategories or bins of data and/or labels associated therewith. Aclassifier may be configured to output at least a datum that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric as described below, or the like.Machine-learning module 1700 may generate a classifier using aclassification algorithm, defined as a processes whereby a computingdevice and/or any module and/or component operating thereon derives aclassifier from training data 1704. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naïve Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 17 , machine-learning module 1700 may beconfigured to perform a lazy-learning process 1720 and/or protocol,which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 1704.Heuristic may include selecting some number of highest-rankingassociations and/or training data 1704 elements. Lazy learning mayimplement any suitable lazy learning algorithm, including withoutlimitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

Alternatively or additionally, and with continued reference to FIG. 17 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 1724. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 1724 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 1724 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 1704set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 17 , machine-learning algorithms may include atleast a supervised machine-learning process 1728. At least a supervisedmachine-learning process 1728, as defined herein, include algorithmsthat receive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude an order 108, an initial location 112, a terminal location 116,aeronautic paths 124, and the like as described above as inputs,autonomous functions as outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 1704. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 1728 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 17 , machine learning processes may include atleast an unsupervised machine-learning processes 1732. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 17 , machine-learning module 1700 may bedesigned and configured to create a machine-learning model 1724 usingtechniques for development of linear regression models. Linearregression models may include ordinary least squares regression, whichaims to minimize the square of the difference between predicted outcomesand actual outcomes according to an appropriate norm for measuring sucha difference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 17 , machine-learning algorithms mayinclude, without limitation, linear discriminant analysis.Machine-learning algorithm may include quadratic discriminate analysis.Machine-learning algorithms may include kernel ridge regression.Machine-learning algorithms may include support vector machines,including without limitation support vector classification-basedregression processes. Machine-learning algorithms may include stochasticgradient descent algorithms, including classification and regressionalgorithms based on stochastic gradient descent. Machine-learningalgorithms may include nearest neighbors algorithms. Machine-learningalgorithms may include Gaussian processes such as Gaussian ProcessRegression. Machine-learning algorithms may include cross-decompositionalgorithms, including partial least squares and/or canonical correlationanalysis. Machine-learning algorithms may include naïve Bayes methods.Machine-learning algorithms may include algorithms based on decisiontrees, such as decision tree classification or regression algorithms.Machine-learning algorithms may include ensemble methods such as baggingmeta-estimator, forest of randomized tress, AdaBoost, gradient treeboosting, and/or voting classifier methods. Machine-learning algorithmsmay include neural net algorithms, including convolutional neural netprocesses.

For example, and still referring to FIG. 17 , neural network also knownas an artificial neural network, is a network of “nodes,” or datastructures having one or more inputs, one or more outputs, and afunction determining outputs based on inputs. Such nodes may beorganized in a network, such as without limitation a convolutionalneural network, including an input layer of nodes, one or moreintermediate layers, and an output layer of nodes. Connections betweennodes may be created via the process of “training” the network, in whichelements from a training dataset are applied to the input nodes, asuitable training algorithm (such as Levenberg-Marquardt, conjugategradient, simulated annealing, or other algorithms) is then used toadjust the connections and weights between nodes in adjacent layers ofthe neural network to produce the desired values at the output nodes.This process is sometimes referred to as deep learning.

Still referring to FIG. 17 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Referring to FIG. 18 , an exemplary method 1800 for aeronautic pathoptimization. Method 1800 includes a step 805, of receiving, using atleast a server, at least an order, wherein the order comprises dataregarding the physical transport of a non-sapient payload from aninitial location to a terminal location. This may occur as describedabove in reference to FIGS. 1-18 . In an embodiment, the at least aserver may further be configured to classify initial location and aterminal location to a geographic centroid using a centroid classifier.In another embodiment, the order maybe transported using an aeronauticexcursion.

With continued reference to FIG. 18 , method 1800 includes a step 1810,of identifying, using the at least a server, an aeronautic path as afunction of the at least an order using a path selection module, whereinthe aeronautic path comprises travel between the initial location andthe terminal location. This may occur as described above in reference toFIGS. 1-18 . In an embodiment, a aeronautic path may be configured totransport the order between the initial location and a geographiccentroid. In another embodiment, the aeronautic path additionally isconfigured to transport the order between a geographic centroid and theterminal location. The geographic centroid may be comprised of aplurality of centroids. The aeronautic path may be configured totransport the order using a mixed payload flight

With continued reference to FIG. 18 , method 1800 includes a step 1815,of generating, using the at least a server, a plurality of optimalroutes for the at least an order as a function of the aeronautic path.This may occur as described above in reference to FIGS. 1-18 . In anembodiment, the optimal route maybe generated using an optimizationmachine learning model. In another embodiment, am optimal route rankingmay be generated as a function of the optimized route.

With continued reference to FIG. 18 , method 1800 includes a step 1820,of producing, using the at least a server, an assigned route as afunction of the plurality of optimal routes. This may occur as describedabove in reference to FIGS. 1-18 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 19 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1900 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1900 includes a processor 1904 and a memory1908 that communicate with each other, and with other components, via abus 1912. Bus 1912 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1904 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1904 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1904 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 1908 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1916 (BIOS), including basic routines thathelp to transfer information between elements within computer system1900, such as during start-up, may be stored in memory 1908. Memory 1908may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1920 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1908 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1900 may also include a storage device 1924. Examples ofa storage device (e.g., storage device 1924) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1924 may beconnected to bus 1912 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1924 (or one or more components thereof) may be removably interfacedwith computer system 1900 (e.g., via an external port connector (notshown)). Particularly, storage device 1924 and an associatedmachine-readable medium 1928 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1900. In one example,software 1920 may reside, completely or partially, withinmachine-readable medium 1928. In another example, software 1920 mayreside, completely or partially, within processor 1904.

Computer system 1900 may also include an input device 1932. In oneexample, a user of computer system 1900 may enter commands and/or otherinformation into computer system 1900 via input device 1932. Examples ofan input device 1932 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1932may be interfaced to bus 1912 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1912, and any combinations thereof. Input device 1932may include a touch screen interface that may be a part of or separatefrom display 1936, discussed further below. Input device 1932 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1900 via storage device 1924 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1940. A networkinterface device, such as network interface device 1940, may be utilizedfor connecting computer system 1900 to one or more of a variety ofnetworks, such as network 1944, and one or more remote devices 1948connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1944, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1920, etc.) may be communicated to and/or fromcomputer system 1900 via network interface device 1940.

Computer system 1900 may further include a video display adapter 1952for communicating a displayable image to a display device, such asdisplay device 1936. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1952 and display device 1936 maybe utilized in combination with processor 1904 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1900 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1912 via a peripheral interface 1956.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for aeronautic path optimization, thesystem comprising: at least a server, wherein the at least a server isconfigured to: receive at least an order, wherein the at least an ordercomprises data regarding a physical transport of a non-sapient payloadfrom an initial location to a terminal location; identify an aeronauticpath as a function of the at least an order using a path selectionmodule, wherein the aeronautic path comprises travel between the initiallocation and the terminal location; generate a plurality of optimalroutes for the at least an order as a function of the aeronautic path;and produce an assigned route as a function the plurality of optimalroutes.
 2. The system of claim 1, wherein the aeronautic path isconfigured to transport the order between the initial location and ageographic centroid.
 3. The system of claim 2, wherein the aeronauticpath additionally is further configured to transport the order betweenthe geographic centroid and the terminal location.
 4. The system ofclaim 1, wherein the aeronautic path is configured to transport theorder between a first geographic centroid and a second geographiccentroid.
 5. The system of claim 1, wherein the aeronautic path isconfigured to transport the at least an order using a mixed payloadflight.
 6. The system of claim 1, wherein the plurality of optimalroutes is generated as a function of an optimization machine learningmodel.
 7. The system of claim 1, wherein the at least a server isfurther configured to classify the initial location to a geographiccentroid as a function of a centroid classifier.
 8. The system of claim1, wherein the at least a server is further configured to classify theterminal location to a geographic centroid as a function of a centroidclassifier.
 9. The system of claim 1, wherein the at least an order istransported using an aeronautic excursion.
 10. The system of claim 1,wherein an optimal route ranking is generated as a function of theplurality of optimal routes.
 11. A method for aeronautic pathoptimization, the method comprising: receiving, using at least a server,at least an order, wherein the at least an order comprises dataregarding a physical transport of a non-sapient payload from an initiallocation to a terminal location; identifying, using the at least aserver, an aeronautic path as a function of the at least an order usinga path selection module , wherein the aeronautic path comprises travelbetween the initial location and the terminal location; generating,using the at least a server, a plurality of optimal routes for the atleast an order as a function of the aeronautic path; and producing,using the at least a server, an assigned route as a function of theplurality of optimal routes.
 12. The method of claim 11, wherein theaeronautic path is configured to transport the order between the initiallocation and a geographic centroid.
 13. The method of claim 12, whereinthe aeronautic path is further configured to transport the order betweenthe geographic centroid and the terminal location.
 14. The method ofclaim 13, wherein the aeronautic path is configured to transport theorder between a first geographic centroid and a second geographiccentroid.
 15. The method of claim 11, wherein the aeronautic path isconfigured to transport the at least an order using a mixed payloadflight.
 16. The method of claim 11, wherein the plurality of optimalroutes is generated as a function of an optimization machine learningmodel.
 17. The method of claim 11, further comprising classifying, usingthe at least a server, the initial location to a geographic centroid asa function of a centroid classifier.
 18. The method of claim 11, furthercomprising classifying, using the at least a server, the terminallocation to a geographic centroid as a function of a centroidclassifier.
 19. The method of claim 11, wherein the at least an order istransported using an aeronautic excursion.
 20. The method of claim 11,wherein an optimal route ranking is generated as a function of theplurality of optimal routes.